I will try to be more specific Let's say that I have a diary data from several subjects. That is my data has a structure of days nested within persons. Let's say that my hypothesis is that daily mood will be predicted by daily positive and negative events. So I want to use multi-level models analyses (MLM/HLM) with proc mixed to test my hypotheses. My model would be something like this level 1: MOODjt= b 0j + b 1j *positive event + b 2j* negative event + e ij (j=person j, t=day t) And the level 2 (random) of the model would be- Level 2: b 0j = γ 00 + u oj b 1j = γ 10 + u 1j b 2j = γ 20 + u 2j To test this model I would use the following proc mixed syntax proc mixed covtest data=X; class id day; model mood= positive_event negative_event/s ; random intercept positive_event negative_event/subject = id; repeated day/subject = id type = ar(1); run; my question is if I want to constrain the positive_event and Negative_event to have the same weights (let's say of 1) in order to test if they are different from each other (that is, if the constrained model has less fit than the unconstrained model), how can I do that? Put it more simply, if I want that a certain fix effect will have specific value in order to test how it influences the fit of the model, how can I do it? Thanks a lot Eran
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